A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
An edge-based approach to motion detection
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
MTES: visual programming environment for teaching and research in image processing
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
An algorithm to estimate mean traffic speed using uncalibrated cameras
IEEE Transactions on Intelligent Transportation Systems
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Efficient moving object segmentation algorithm using background registration technique
IEEE Transactions on Circuits and Systems for Video Technology
Moving-edge detection via heat flow analogy
Pattern Recognition Letters
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Reference update to adapt with the dynamism of environment is one of the most challenging tasks in moving object detection for video surveillance. Different background modeling techniques have been proposed. However, most of these methods suffer from high computational cost and difficulties in determining the appropriate location as well as pixel values to update the background. In this paper, we present a new algorithm which utilizes three most recent successive frames to isolate moving edges for moving object detection. It does not require any background model. Hence, it is computationally faster and applicable for real time processing. We also introduce segment based representation of edges in the proposed method instead of traditional pixel based representation which facilitates to incorporate an efficient edge-matching algorithm to solve edge localization problem. It provides robustness against the random noise, illumination variation and quantization error. Experimental results of the proposed method are included in this paper to compare with some other standard methods that are frequently used in video surveillance.